1. Field of the Invention
The invention relates generally to signal processing to reduce the effects of noise and particularly to a Least Mean Square (LMS) vibration/noise control algorithm. Still more particularly to a Least Mean Square vibration/noise control algorithm that eliminates the requirement for a reference sensor to generate a reference signal.
2. Description of the Related Art
Active noise or disturbance attenuation has been a high priority issue for many years for applications such as acoustic systems and industrial equipment. The advance of optical laser systems and their increased usage in satellites, space missions, imaging systems, communication and many military applications have established a new trend towards a more critical look at active disturbance control systems. Ever growing demands such as arc-second accuracy and nano-radian jitter require precise and efficient control systems. The growing widespread use of lasers for communications, space and military missions and the increased requirements on the specifications such as precise pointing have demanded efficient optical control methods in recent years. Unlike other communication media such as radio wave, which spreads in a spherical pattern, precision pointing and jitter control are very crucial to efficient laser communications systems. This is mainly because the presence of jitter reduces the intensity of the laser beam and causes fluctuations in the optical beam. The environmental factors such as the atmosphere and the structural interactions that cause vibrations to laser beams often add unwanted fluctuations to optical laser beams. The effect of the atmosphere on the laser beam is considered very serious because it adds broadband disturbance to optical lasers.
The control of a disturbance or noise has its origin in the areas of acoustics and structures. The use of passive systems that blanket the area with material that would absorb the noise frequencies and the use of damping components to reduce the structural vibrations are some of the commonly applied noise or vibration control techniques. Unfortunately, these techniques cannot be applied to control jitter on optical laser beams due to the time-varying characteristics of disturbances and other obvious reasons such as size and weight limitations.
Adaptive noise control algorithms have been successfully applied to reduce noise in many acoustic systems for many years. Since the noise source and the environment are time varying in general, it is often desired that an active noise control system be adaptive. Furthermore, the use of adaptive filters in the noise control systems has been proven to be low cost and very efficient. Moreover, the recent advances in signal processing and the availability of Digital Signal Processor (DSP) chips have enhanced the practicality of the adaptive filters. Adaptive filters and their applications have been widely studied by many researchers in the past. The basic idea is to design a digital filter such that its output while being passed through the system generates an antinoise component of equal amplitude and opposite phase. According to the principle of superposition, noise and antinoise components are combined to cancel each other resulting in noise elimination or reduction.
Adaptive filters are designed by minimizing an error function and can be realized as Finite Impulse Response (FIR), Infinite Impulse Response (IIR) or lattice and transform-domain filters. The most commonly used adaptive filter is the FIR filter using a Least Mean Square (LMS) algorithm. In this method, the adaptive noise cancellation is achieved through two distinct operations: 1) a digital FIR is used to perform the desired signal processing, and 2) an adaptive LMS algorithm is used to adjust the coefficients of the digital filter. An FIR filter is a digital filter that in response to a Kronecker delta input produces a response that settles to zero in a finite number of sample intervals. An Nth order FIR filter has a response to an impulse that is N+1 samples in duration. This is in contrast to IIR filters that have internal feedback and may continue to respond indefinitely. The input and output signals for an FIR filter are related by the difference equation
            y      ⁡              (        n        )              =                                        b            0                    ⁢                      x            ⁡                          (              n              )                                      +                              b            1                    ⁢                      x            ⁡                          (                              n                -                1                            )                                      +        …        +                              b            N                    ⁢                      x            ⁡                          (                              n                -                N                            )                                          =                        ∑                      i            =            0                    N                ⁢                                  ⁢                              b            i                    ⁢                      x            ⁡                          (                              n                -                i                            )                                            ,where x(n) is the input signal, y(n) and bi are the filter coefficients.
A serious issue associated with the prior art implementations of the LMS algorithm for noise cancellation is the requirement of a coherent reference signal, which must be well correlated with the disturbance or noise. A common practice is to measure the disturbance or noise directly and use it as the reference signal to the LMS algorithm. A direct measurement of disturbance may not be possible always and even if it is possible, it will require that additional resources be used and eventually increase the cost of the operation or process.